Automatic Defect Classification (ADC) techniques are widely used in inspection and measurement of defects on patterned wafers in the semiconductor industry. The objective of these techniques is not only to detect the existence of defects, but to classify them automatically by type, in order to provide more detailed feedback on the production process and reduce the load on human inspectors. ADC is used, for example, to distinguish among types of defects arising from particulate contaminants on the wafer surface and defects associated with irregularities in the microcircuit pattern itself, and may also identify specific types of particles and irregularities.
Various methods for ADC have been described in the patent literature. For example, U.S. Pat. No. 6,256,093, whose disclosure is incorporated herein by reference, describes a system for on-the-fly ADC in a scanned wafer. A light source illuminates the scanned wafer so as to generate an illuminating spot on the wafer. Light scattered from the spot is sensed by at least two spaced-apart detectors, and is analyzed so as to detect defects in the wafer and classify the defects into distinct defect types.
As another example, U.S. Pat. No. 6,922,482, whose disclosure is incorporated herein by reference, describes a method and apparatus for automatically classifying a defect on the surface of a semiconductor wafer into one of a number of core classes, using a core classifier employing boundary and topographical information. The defect is then further classified into a subclass using a specific adaptive classifier that is associated with the core class and trained to classify defects from only a limited number of related core classes. Defects that cannot be classified by the core classifier or the specific adaptive classifiers are classified by a full classifier.
Various mathematical techniques have been applied in ADC schemes. For example, Glazer and Sipper describe an evolutionary classification tool, based on genetic algorithms (GAs), in “Evolving an Automatic Defect Classification Tool,” EvoWorkshops 2008, LNCS 4974 (Springer-Verlag, 2008), pages 194-203, which is incorporated herein by reference. The article shows that GA-based models can attain better classification performance, with lower complexity, than human-based and heavy random search models.
Automatic defect classification (ADC) systems are commonly calibrated using a set of training data, containing a collection of defects that have been pre-classified by a human expert. The ADC system uses the training data in order to set respective ranges of parameter values that are associated with each defect class in a multi-dimensional parameter space (also referred to as a hyperspace when classification involves more than three parameters). In most existing systems, these settings are then tested and adjusted to optimize their accuracy, which is defined as the percentage of all defects that are classified correctly.
In many ADC applications, however, purity of classification is a more meaningful measure of system operation. The system operator may specify a certain maximum rejection rate, i.e., a percentage of the defects that the ADC system is unable to classify with confidence and therefore returns to the system operator for classification by a human expert. “Purity” refers to the percentage of the remaining defects—those found by the ADC system to be classifiable and not rejected—that are classified correctly. Since it is realistic to assume that there will always be some percentage of defects that is rejected by the ADC system, purity is the measure that is actually of greatest concern to the operator.
Purity of classification can be affected by various kinds of classification uncertainty. In some cases, the parameter values associated with a defect may fall in a region of overlap between two (or more) different classes. In others, the parameter values of the defect may lie at the outer borders of the range associated with a given class.